Papers by Toshiaki Wakatsuki

3 papers
Integrating Pre-Trained Speech and Language Models for End-to-End Speech Recognition (2024.findings-acl)

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Challenge: Mainstream of automatic speech recognition (ASR) has shifted from pipeline methods to end-to-end (E2E) methods.
Approach: They propose to integrate a pre-trained speech representation model and a large language model (LLM) for automatic speech recognition in an end-to-end manner.
Outcome: The proposed model achieves comparable performance to modern E2E ASR models by utilizing powerful pre-training models with the proposed integrated approach.
Release of Pre-Trained Models for the Japanese Language (2024.lrec-main)

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Challenge: democratization of AI aims to create a world where everyone can use AI . pre-trained models with high performance in Japanese are lagging in non-English-speaking communities .
Approach: et al. released large-scale pre-trained models trained on large-data to improve access to AI . authors say the models are more accurate and more accurate than those trained in the English language . e-mail protected: email protected.
Outcome: a new study shows that pre-trained models specialized for Japanese can achieve high performance in Japanese tasks.
PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems (2024.findings-emnlp)

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Challenge: Existing models that process both text and speech face problems in response generation latency.
Approach: They propose to extend the input and output sequences of the language model to support the parallel generation of text and speech.
Outcome: The proposed model improves latency while maintaining quality of response content while maintaining the quality of the response content.

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